Data Science  

What SQL Topics Are Important for Data Science Interviews?

Introduction 🌟

If you are preparing for a data science interview, one skill you can’t ignore is SQL (Structured Query Language). Data scientists work with databases daily, and companies expect you to know how to query, clean, and analyze data using SQL. But here’s the big question: What SQL topics are actually important for data science interviews? In this article, we’ll cover the essential SQL concepts in simple words so you can prepare smartly and impress interviewers.

1. SQL Basics πŸ“˜

Before diving into advanced topics, you must understand the fundamentals:

  • SELECT statements: How to get data from a database.

  • WHERE clause: Filtering rows based on conditions.

  • ORDER BY: Sorting results.

  • LIMIT: Returning only a set number of rows.

πŸ‘‰ Interviewers often start with basic SQL queries to test your foundation.

Example

SELECT name, age
FROM employees
WHERE age > 30
ORDER BY age DESC
LIMIT 5;

2. Joins in SQL πŸ”—

Joins are one of the most common SQL topics in interviews because real-world data is spread across multiple tables.

  • INNER JOIN: Returns matching rows from both tables.

  • LEFT JOIN: Returns all rows from the left table and matching rows from the right.

  • RIGHT JOIN: Opposite of LEFT JOIN.

  • FULL OUTER JOIN: Combines all rows from both tables.

πŸ‘‰ Be prepared to explain and write queries using different joins.

Example

SELECT c.customer_id, o.order_id
FROM customers c
INNER JOIN orders o ON c.customer_id = o.customer_id;

3. Aggregations & Group By πŸ“Š

Companies want data scientists who can summarize data. You should know:

  • COUNT()

  • SUM()

  • AVG()

  • MIN() / MAX()

  • GROUP BY

  • HAVING (filter groups after aggregation)

πŸ‘‰ These are key for analyzing business metrics in interviews.

Example

SELECT department, AVG(salary) AS avg_salary
FROM employees
GROUP BY department
HAVING AVG(salary) > 50000;

4. Subqueries & CTEs 🧩

Sometimes, you need to use the result of one query inside another.

  • Subqueries: Queries inside SELECT, FROM, or WHERE.

  • CTEs (Common Table Expressions): Cleaner way to write complex queries using WITH.

πŸ‘‰ Interviewers test this to check your ability to structure complex problems.

Example

WITH sales_cte AS (
  SELECT customer_id, SUM(amount) AS total_sales
  FROM sales
  GROUP BY customer_id
)
SELECT * FROM sales_cte WHERE total_sales > 1000;

5. Window Functions πŸ”

Window functions are advanced SQL concepts, but very important for data science roles:

  • ROW_NUMBER(): Assigns a unique number to rows.

  • RANK() / DENSE_RANK(): Useful for finding top performers.

  • OVER() with PARTITION BY: Analyze data within groups.

  • Running totals & moving averages.

πŸ‘‰ These questions show up in many technical interviews because they reflect real-world analytics.

Example

SELECT employee_id, salary,
       RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS dept_rank
FROM employees;

6. Data Cleaning & String Functions 🧹

Data is often messy. Knowing how to clean it with SQL is a big plus:

  • TRIM(), UPPER(), LOWER()

  • SUBSTRING()

  • REPLACE()

  • CAST() / CONVERT()

  • Handling NULL values with COALESCE() or ISNULL().

πŸ‘‰ Interviewers like to test if you can transform raw data into useful insights.

Example

SELECT COALESCE(phone_number, 'Not Provided') AS contact
FROM customers;

7. SQL for Performance Optimization ⚑

In advanced interviews, you may be asked about performance:

  • Indexes: How they speed up queries.

  • EXPLAIN plans: Understanding how queries are executed.

  • Avoiding unnecessary subqueries.

πŸ‘‰ Even basic awareness of optimization makes you stand out as a prepared candidate.

8. Real-World Business Scenarios πŸ’Ό

Finally, expect case-study style questions:

  • Find the top 5 products by sales.

  • Identify customers who haven’t purchased in the last 6 months.

  • Calculate monthly active users (MAU).

πŸ‘‰ Practicing real-world scenarios will help you apply SQL logically in interviews.

9. SQL Interview Practice Q & A 🎯

Here are some common SQL interview questions you can practice:

Q 1. Find the second highest salary in the employees table.

SELECT MAX(salary) AS second_highest
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);

Q 2. Find customers who have placed more than 5 orders.

SELECT customer_id, COUNT(order_id) AS total_orders
FROM orders
GROUP BY customer_id
HAVING COUNT(order_id) > 5;

Q 3. Get the top 3 products by sales amount in each category.

SELECT product_id, category,
       RANK() OVER (PARTITION BY category ORDER BY SUM(amount) DESC) AS rank_in_category
FROM sales
GROUP BY product_id, category
HAVING RANK() <= 3;

Q 4. Find employees who don’t have a manager assigned.

SELECT employee_id, name
FROM employees
WHERE manager_id IS NULL;

πŸ‘‰ Practicing these types of problems improves your speed and confidence.

Conclusion βœ…

To succeed in a data science interview in 2025, you must focus on the most important SQL topics:

  • Basics of SELECT, filtering, and sorting

  • Joins

  • Aggregations & Group By

  • Subqueries & CTEs

  • Window functions

  • Data cleaning with string functions

  • Performance optimization

  • Real-world problem solving

  • Hands-on practice with interview-style SQL questions

By mastering these, you’ll be ready to handle almost any SQL question confidently. Remember: practice writing queries on real datasets β€” because interviewers want to see not just theory, but how you apply SQL to real-world data science problems.

✨ Final Tip: Use platforms like LeetCode, HackerRank, or Mode Analytics to practice SQL interview questions regularly!

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